There has been tremendous evolution in the sphere of accommodating and synthesizing data from a very wide variety of sources for the purpose of seeking creative and broad-based solutions to complex local problems. For instance, addressing traffic management problems in one city can benefit from assimilating data and solutions from other cities not only from the same country but from throughout the world. Such broad-based smart or intelligent problem-solving is at the heart of efforts such as, e.g., “Solutions for a Smarter Planet” (or “Smarter Planet”) by International Business Machines (IBM) of Armonk, N.Y.
However, in pursuing broad-based solutions as discussed above, problems have often been encountered in retrieving data from diverse and disparate instrumentation and/or other input sources, leading to costly, slow, noisy, insufficient and error-prone data retrieval and synthesis. Physical and communicative constraints and limitations thus often provide roadblocks to offering the smart solutions and intelligent results that otherwise could make a critical difference in addressing local challenges in complex arenas such as traffic and infrastructure.